I'm an ML engineer from France with experiences in MLOps, computer vision, NLP and web application development.

• Recommendation System with Variational Autoencoder (VAE) - TensorFlow 2, MLflow

A VAE-based Recommender System designed to provide personalized recommendations for users based on their historical interactions with items (collaborative filtering). The recommender is tested against the user-item interactions from the Movies Lens 20M dataset. Training models are versioned by MLflow.

• Perfume Reviews Analysis - NLP, Dash App

Example of Dash application developed for the analysis of 20k+ multi-language product reviews (sentiment analysis, word frequency and semantic similarity). NLP tools include Spacy, NLTK, TextBlob and Marian transformers (machine translation).

• Deep learning app deployment (module frontend + backend)

Simple module of a web application with deep learning (vision system) as a service: asynchronous processing of long tasks and continuous streaming of information to client side. Backend server setup and fromt-end development. Tools: Flask, Flask-SocketIO, Celery, jQuery, AJAX, SocketIO.

• Mask RCNN written in Tensorflow 2 (+ integration testing)

A custom-built mask-rcnn model written with TensorFlow 2, and associated integration testing module written with PyUnit.

• Transaction fraud detection (Pandas + multiprocessing)

A data science project to predict whether a transaction is a fraud or not. Processing of 200,000+ transaction records, feature engineering and data analysis. Learning models are trainer and tested (Random Forest, k-Nearest Neighbour, multi-layer perceptrons).

• 3D detection in point cloud data via Graph Convolutional Networks (GCNs)

The algorithm addresses the 3D object detection task from point cloud sonar data to capture specified terrain information with Graph Convolutional Networks (GCNs) and a deep voting module.

• Object-based Probabilistic Similarity Evidence (OPSE) of Latent Features from FCNs

OPSE is an algorithm processing the latent characteristics of fully convolutional networks (FCNs) to group objects with analogous properties. Its application is demonstrated using the UT Zappos50K datasets.

• Predicting Oil and Water Productions from Time Series Data

A time series forecasting GRU model is proposed to estimate the oil and water productions from 2 wells. 3400+ records from 2009 to 2011. Jupyter Notebook project.

• Wasserstein Generative Adversarial Network (WGAN)

A simple implementation of WGAN written with TensorFlow 2.

• Python: TensorFlow 2, Keras, scikit-learn, Pandas, NumPy, threading, asyncio, Requests, Multiprocessing, Dash Plotly and Matplotlib.

• Software development: OOP, TDD, version control Git, CI, MLOps, QA testing (PyUnit), Agile practices (standups), code linting and documentation.

• Web app development: Flask, Flask-Restful, jQuery, JavaScript, Docker, Socker.IO/WebSocket API, task queuing (Celery), AJAX, HTML and CSS, UI design.

• Computer vision: 2D/3D detection, generative AI (GANs, VAE), image processing and OpenCV, semi-supervised learning and object clustering.

• NLP: Spacy, TextBlob, NLTK, HuggingFace Hub.

• Deep learning: building models, transfer-learning, literature review and implementation, network analysis and performance optimization.

• Machine learning: clustering, regression, classification, dimensionality reduction, ensemble methods, supervised/unsupervised learning.

• Data Collection and Storage: MySQL, SQLite, PyMySQL and SQLAlchemy.